Scalable and Explainable Linear Shallow Autoencoders for Collaborative Filtering from Industrial Perspective

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00368607" target="_blank" >RIV/68407700:21240/23:00368607 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3565472.3595630" target="_blank" >https://doi.org/10.1145/3565472.3595630</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3565472.3595630" target="_blank" >10.1145/3565472.3595630</a>

Result language

angličtina

Original language name

Scalable and Explainable Linear Shallow Autoencoders for Collaborative Filtering from Industrial Perspective

Original language description

The popularity of linear shallow autoencoders for collaborative filtering is growing in the research community, and internet industry providers of Recommender Systems are also taking notice. However, despite their simplicity and accuracy, these models often cannot be used in real-world industrial recommender systems due to their inability to scale to very large interaction matrices. Our research aims to address this issue by developing a scalable, explainable, and accurate shallow linear autoencoder method for collaborative filtering that meets the demands of real-world recommenders. In this paper, we present our industrial Ph.D. research project, which includes: (1) the development of a scalable method called ELSA and the adaptation of the method to a large real-world recommender and (2) the creation of a framework to visualize the recommender systems insights based on modeling the distribution of retrieval metrics in latent user space. We discuss the current status of our project, the key steps to finish the project, and the possible future extensions after the dissertation.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

ISBN

978-1-4503-9932-6

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

290-295

Publisher name

Association for Computing Machinery

Place of publication

New York

Event location

Limassol

Event date

Jun 26, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001051715400031